Identification of filler type in cavities behind tunnel linings during a subway tunnel surveys using the impulse-response method

Вознесенский, А. С.; Набатов, В. В.

doi:10.1016/j.tust.2017.07.010

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…The parameter values at the points are plotted on the contact quality map and the values between the points are interpolated. A more detailed survey method description is given in [35,36]. Figure 1а shows an example of the contact quality map at the soil-reinforced concrete tunnel lining interface.…”

Section: Methodsmentioning

confidence: 99%

Influence of soil–tunnel lining interface on its control by acoustic response to impulse impact

Nabatov¹,

Voznesenskii²

2022

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The purpose of this study is to confirm the assumption that the stress-strain behavior of soil body at the interface with lining can be the cause of the cavity formation behind the lining space. The plastic deformation zones in soil from the computer modelling geometrically agree with the locations of cavities detected by the in-situ geophysical measurements using the method of impact response. This suggests that the stress-strain behavior and transition of soil body into a plastic strain state can cause cavity formation. The cavity formation alters acoustic response of the lining to impact, which is used to detect cavities.

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Section: Methodsmentioning

confidence: 99%

Influence of soil–tunnel lining interface on its control by acoustic response to impulse impact

Nabatov¹,

Voznesenskii²

2022

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“…Choo et al [8] used the GPR detection method to analyze the thickness of NATM tunnel lining and the conditions behind the lining, and discovered that void is very likely to occur between the secondary lining and initial support near the tunnel crown. Voznesenskii and Nabatov [9] used the impact sound response to develop a new method to identify the type of flling body in the void behind tunnel lining. Ren et al [10] used the fnite element modeling technique to analyze how the size and position of void and the multi-void combination efect afect the tunnel lining structure.…”

Section: Introductionmentioning

confidence: 99%

Study on Impact of Various Void Conditions on Shallow Rectangular Masonry-Concrete Power Tunnel and Surrounding Roads

Tang

Jin

Zhang

et al. 2023

Advances in Civil Engineering

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To solve the problem of a potential void near a shallow rectangular masonry-concrete power tunnel, the ground-penetrating radar (GPR) detection technology is used and multiple GPR detection lines are placed to detect concealed void. The GPR images of circular and square voids are obtained, which can help increase accuracy of void identification. The models that cover 9 circular voids and 9 square voids are created to investigate how various forms and positions of void will affect the internal forces of the power tunnel structure and surrounding roads. In terms of morphological characteristics, according to the research results, the impact of a circular void on tunnel and surrounding roads is slightly stronger than that of a square void. If the circular void and square void are at 135° and 115°, respectively, the maximum shear force of the tunnel structure occurs at the floor. If both circular and square voids are at 115°, the maximum axial force of tunnel structure occurs at the right side wall. If both circular and square voids are at 135°, the maximum bending moment of tunnel structure occurs at the floor. If both circular and square voids are at 155°, the ground settlement and pavement stress of surrounding roads are maximized. Generally, if a void occurs at the middle or lower part of the side wall or near the side of the floor in a shallow rectangular masonry-concrete power tunnel, its adverse impact is at its worst. Therefore, GPR detection of the voids at the lower and middle parts and the side of the floor in the power tunnel should be intensified, in order to eliminate the risk of a void near power tunnel.

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“…To detect the health status of the tunnel, researchers used ultrasonic and radar sensors to calculate the thickness of the lining, the voids and cracks behind the lining [50].…”

Section: Introductionmentioning

confidence: 99%

MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity

Zhang

Wu²,

Zhao

et al. 2020

IEEE Access

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Testing the health of tunnels, as a branch of highway operation, has an extremely important application in public property and even life safety. Among them, there are many factors that cause the tunnel to deform or collapse. The conventional methods use the finite element method (FEM) which are to simulate the bearing capacity loss rate of the lining by using the mechanical method. However, it takes a long time to calculate the stress-strain-situation of the lining model under each condition. This paper explores the machine learning to calculate the loss rate of the lining bearing capacity under more conditions based on FEM simulation data. Here, we establish a machine learning toolbox for modeling the loss rate of the lining bearing capacity named ''MLLBC'', which contains three main components: 1) data loading; 2) machine learning model deployment; 3) performance evaluation. To ensure the fairness of model evaluation, ten machine learning models use a unified code library. We also conduct experiments on our new dataset which is the loss rate of the lining bearing capacity with different data amounts, as well as experiments on the goodness of model fitting under different ranges of various variables. INDEX TERMS Toolbox, the loss rate of the lining bearing capacity, machine learning, tunnel health.

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Identification of filler type in cavities behind tunnel linings during a subway tunnel surveys using the impulse-response method

Cited by 10 publications

References 28 publications

Influence of soil–tunnel lining interface on its control by acoustic response to impulse impact

Influence of soil–tunnel lining interface on its control by acoustic response to impulse impact

Study on Impact of Various Void Conditions on Shallow Rectangular Masonry-Concrete Power Tunnel and Surrounding Roads

MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity

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